The WSR-88D has proven itself as a valuable tool when attempting to locate mesocyclones (mesos)
and possible tornadoes. With the capability to view inbound and outbound (IO) velocities (via the
Storm Relative Velocity Map [SRM]) and storm structure (through multiple elevation scans), radar
operators are able to issue Tornado Warnings well before tornadoes are spawned. However, only
about 30% of all meso cores produce tornadoes (OSF 1997) and there is no guarantee that tornadoes
will develop even when certain criteria are met (such as a moderate to strong meso which is at least
10,000 ft deep for two or more volume scans).

A more complex problem involves determining when and if tornadoes have been produced, and
when those tornadoes will dissipate. To help solve such a problem, three tornadic supercells were
examined Fig. 1 after the March 1, 1997 tornado outbreak in Arkansas by comparing archived
WSR-88D graphical and alphanumeric parameters with ground and aerial surveys. It was hoped that
some of WSR-88D data sets would correlate well with survey results. If so, tornado touchdowns and
dissipations might be predicted.

2. The Tracks

The Arkadelphia/Little Rock/Hickory Ridge Storm (Supercell 1)

Supercell 1 traveled through rolling terrain during the first half of its journey (through Little Rock)
with no large elevation changes Fig. 2. The storm moved through forested areas at times, but
remained east of and tracked parallel to the Ouachita National Forest. During the second half of its
course (north and east of Little Rock), the storm encountered minor obstacles with mainly farmland
and a few trees. From surveys and gathered data, it was determined that a tornado first touched
down about two miles northeast of Hope in southwest Arkansas at 1950 UTC. The tornado was
small at first, with ratings from F0 to F2. As it approached Arkadelphia (around 2042 UTC), the
tornado strengthened to F4 intensity and widened to about 0.5 mi in diameter. As the tornado neared
Malvern, it began to weaken and dissipated around 2110 UTC (path length 67 mi).

The storm moved about 12 mi before another tornado was produced three miles southeast of Benton
at 2125 UTC. This tornado quickly intensified to F4 and widened to as much as 0.8 mi as it tracked
just south of downtown Little Rock at 2145 UTC. The tornado weakened and dissipated 4 mi east
of the city about 10 minutes later (path length 27 mi). At around 2205 UTC, a weak (F1 to F2)
tornado was spawned near Furlow, and had only a 2.5 mi path. From there, no tornadoes were
produced for 45 min (40 mi).

At 2255 UTC, yet another tornado was spawned near Patterson, which moved into Hickory Ridge
by 2320 UTC (F2 to F3 intensity). The tornado lingered for roughly ten more minutes before
dissipating (path length 25 mi).

The Cabot/Jacksonport/Tuckerman Storm (Supercell 2)

Supercell 2 Fig. 3 had a journey similar to Supercell 1. It encountered rolling terrain and some
forested areas as it first developed, and mostly farmland thereafter (north and east of Searcy). A
weak tornado (F0 to F1) first emerged 3 mi northwest of Cabot at 2035 UTC, and remained weak
as it headed northeast. The tornado dissipated 8 mi southwest of Searcy at around 2055 UTC (path
length 18 mi). No other tornadoes were noted for roughly 18 mi (20 min), when another tornado
spawned at 2115 UTC (10 mi northeast of Searcy). This tornado strengthened to F2 to F3 as it
headed toward Jacksonport. The tornado stayed on the ground and caused considerable damage at
Jacksonport (2145 UTC) before reaching Tuckerman (2200 UTC). The tornado continued moving
into northeast Arkansas before dissipating around 2310 UTC (path length 95 mi).

The Russellville/Jerusalem/Shirley Storm (Supercell 3)

Unlike Supercells 1 and 2, Supercell 3 moved through mostly rough (hilly) terrain and wooded areas
along much of its course Fig. 4. The storm produced a weak tornado (F0 to F1) about 10 mi
northeast of Russellville (near Oak Grove) at 2130 UTC. The tornado strengthened to F2 as it
moved through Jerusalem (around 2140 UTC), but weakened rapidly and dissipated 10 min later
(path length 15 mi). No other tornadoes developed for 25 min (20 mi), but an F0 to F1 tornado
spawned 6 mi east of Clinton at 2215 UTC. The tornado was on the ground for only 2 mi before
dissipating near Shirley at 2220 UTC.

3. The Surveys

Tornado track information was gathered through ground surveys mostly during the first two weeks
of March 1997. Information was obtained by intersecting the tracks at as many points as possible
(where roads were available). Each point was plotted on a map, and the points were connected to
reveal paths. Before surveys were undertaken, radar velocity data were analyzed to get a rough idea
of where tornadoes might have developed. Ground surveys were followed by surveys in the air,
usually at altitudes from 1000 to 2000 ft. Flying at low altitudes was important because details were
not usually vivid. In forested areas, trees were without leaves, so it was difficult to distinguish
between standing and downed timber. Over farmland, buildings and trees were few and spread apart,
making it difficult to find tornado damage.

4. The Data

Both graphical and alphanumeric WSR-88D data were extracted along each known tornado track
for every volume scan that was available. More specifically, meso bases and heights (alphanumeric)
were collected to determine meso depths, and 0.5 deg SRM data (graphical) were used to calculate
meso strengths, circulation widths, meso diameters, and rotational shears. The alphanumeric and
graphical data were then compared with survey information to determine what relationships might
exist with tornado touchdowns and dissipations.

WSR-88D Alphanumeric Products - Meso Base/Top Data

This meso base/meso top data set was collected to calculate meso depths within storms. In general,
deep mesos (at least 10,000 ft) are desired for tornadic development, with the depth sustained for two
or more volume scans (OSF 1996). Unfortunately, meso depths in the cases examined seemed to
show no relationship to whether tornadoes had developed or had dissipated Fig. 5.

In the storms examined (58 volume scans beyond 20 nm of the radar), tornado touchdowns were less
likely to occur (67% of the time) than tornado dissipations (96% of the time) with depths of 10,000
ft or more. In fact, mesos were less deep with tornadoes (13,700 ft) than without tornadoes (17,100
ft). In 11 of 12 volume scans (92%) with mesos less than 10,000 feet deep, there were tornadoes on
the ground.

WSR-88D Graphical Products - SRM Data (0.5 Degree)

Meso Strengths

Meso strengths (strong, moderate, or minimal) were determined by clicking (with the radar puck)
the strongest IO velocities surrounding a meso (less than or equal to 40 kt), selecting the "VR/Shear
Display" button, and applying the radar calculated VR values (in kt) and storm distances from the
radar to a nomogram Fig. 6. For tornadoes, at least a moderate meso is desired (OSF 1996).

As was the case with meso depths (in this study), meso strength data were not helpful when
determining tornado touchdowns and dissipations Fig. 7. (For example, mesos were moderate to
strong in almost all volume scans (62 of 69 scans or 90%), regardless of the situation. With
tornadoes on the ground, mesos were at least moderate in 41 of 41 scans (100%). When tornadoes
had dissipated, mesos remained at least moderate in 21 of 28 scans (75%). However, when
tornadoes had dissipated for more than 30 minutes, moderate to strong mesos were present in only
4 of 9 scans (44%). This suggested that meso strength data might show tornado dissipations more
clearly if they were prolonged.

Circulation Widths

Circulation widths were determined when meso strengths were calculated; that is, the widths were
the number of pixels between the strongest IO velocities (less than or equal to 40 kt). If the strongest
IO velocities were adjacent to one another (gate-to-gate), the circulation was called "very tight." If
the strongest IO velocities were one pixel apart, the circulation was "tight", with IO velocities more
than one pixel apart called "broad" Fig. 8.

Looking at Supercells 1 through 3, circulations were tight or very tight with tornadoes on the ground
in 39 of 41 volume scans (95%). When tornadoes had dissipated, circulations became broad in 24
of 28 volume scans (86%). While correlations were high, there were times when circulations
became broad with tornadoes present, and tight (but not very tight) when there were no confirmed
tornadoes. However, this happened sporadically, with the overall trends showing touchdowns and
dissipations accurately.

Meso Diameter

As described, circulation widths were mostly visual measurements (i.e. number of pixels) between
IO velocities. More exact measurements (with the radar puck) were taken to determine when
tornado spin-ups and spin-downs might occur. The latter measurements were meso diameters, or
circulation widths in nm.

Because circulation widths and meso diameters are similar (except for their units of measure), it
would seem logical that they would have about the same results. More specifically, tornado
touchdowns should be more likely with decreasing meso diameters, with dissipations occurring as
meso diameters increase. The data seemed to support these notions. With meso diameters of 1.2
nm or less within 50 nm of the radar, and 1.6 nm or less beyond 50 nm (due to larger velocity pixels
at a distance from the radar), there was a tornado on the ground in 38 of 41 volume scans (93%).
When diameters became larger than 1.2 nm within 50 nm, and larger than 1.6 nm beyond 50 nm,
tornadoes dissipated in 24 of 28 volume scans (86%). As shown, the meso diameter data performed
favorably when addressing tornado touchdowns and dissipations.

Rotational Shear

Rotational shear is related to meso diameter (and circulation width) in that it increases as meso
diameter decreases and vice-versa Fig. 9. Shear should be strong enough to suggest the presence
of a tornado. The criteria calls for 0.010/sec (greater than 70 nm from the radar) to 0.020/sec (less
than 70 nm from the radar) of shear (Wilken 1997), with .015/sec used in this study for simplicity.

The shear data results closely resembled meso diameter and circulation width results. In 39 of 41
volume scans (95%), shear values were .015/sec or above when a tornado was on the ground. When
tornadoes had dissipated, shear values were less than .015/sec in 24 of 28 volume scans (86%).
Overall trends were good, with the data showing clear indications of touchdowns and dissipations.

5. Looping the Data

Aside from the data in this study, the easiest way to look for possible tornadoes is to get a visual
perspective through radar loops. All three storms were examined through loops by looking at SRM
products (mostly at 0.5 deg). By looping, one can see how velocities increase or decrease with time,
and how circulations tighten or broaden. Still images do not show trends, and offer the radar
operator few clues about what could happen in the next scan. Needless to say, it is of utmost
importance to start a radar loop when in warning mode in order to catch intensifying storms.

6. Conclusion

Diagnosing tornadoes has never been easy, and it still is not. The data sets analyzed reflect this, with
no clear-cut method to resolve tornado touchdowns and dissipations. However, if the most reliable
data are used the odds of identifying tornado spin-ups and spin-downs may increase.

In this study, it was shown that the most success was achieved with circulation width, meso
diameter, and rotational shear. The main reason for the success was that the data sets were related
to one another, with positive results in one data set leading to similar results in the other sets.
Results were not as positive with meso depth and meso strength data. This is mainly because both
data sets remained fairly constant through the study, with no fluctuations that would signify tornado
touchdowns and dissipations.

Of course, the data involved rather large tornadoes (for the most part), which seem to be easier to
discern with the WSR-88D than weak tornadoes. Details with smaller tornadoes might not be as
pronounced in the data as was seen in this study. Therefore, results with smaller tornadoes might
not be as conclusive.

Several factors in this study played a role in affecting the data collected and the conclusions reached.
One factor was terrain, with storms sometimes tracking over hilly and forested areas, and at other
times over regions with very little elevation and few trees. Such a difference in terrain might have
modified the low level inflow, with frictional effects coming into play. The terrain may have
affected when tornado touchdowns and dissipations occurred.

Also a factor in this study was the distance(s) of the storms from the radar. Using the 0.5 deg
elevation scan, data would come from close to the base of a nearby storm, and from the mid-levels
of a storm at a greater distance (beyond 50 nm for example). Because some of the data were
collected from distant storms, conclusions regarding tornado touchdowns or dissipations would be
suspect because any tornadoes would be well below the radar beam.

Yet another factor was the accuracy of the survey results. A lot of time was spent making the
tornado tracks as exact as possible, but it was impossible to cover every inch of where tornadoes
traveled, and it was difficult at times to find details when in flight. Small errors in track information
would affect the timing of tornado touchdowns and dissipations, with the results of this study not
as solid.

A final factor was a lack of radar data; that is, data were available only after the radar volume scans.
A lot can happen between scans that the radar operator never sees, such as brief tornado touchdowns
and dissipations. Of course, radar loops can help operators somewhat overcome missing details
between volume scans. With loops, trends in changing storm strength can be seen, with clues to
where and when tornadoes might occur.

Regardless of how conclusive the results were in this study, or how helpful loops are, the evidence
shows only what data sets were most effective in identifying tornado touchdowns and dissipations.
With so many factors to consider, much more data will have to be collected and more studies
conducted before any warning criteria are changed.

Acknowledgments

The author wishes to thank Mr. George Wilken, Science and Operations Officer (SOO) at NWSFO
Little Rock, for offering suggestions to improve the format and clarity of the paper, and evaluating
its scientific integrity.